As representative state-of-the-art techniques, we also trained image-based U-Net [1]}, DenseUnet [2]} and SEUnet [3]} to compare with our proposed PKT network. DenseUnet used dense connections between U-Net layers and SEUnet added channel-wise attention modules to the U-Net. We reconstructed 2D images with 100 and 400 spokes as the input and output images, respectively. All network training were completed in Python 3.7.7 using the Pytorch library 1.8.1 on a computer with a NVIDIA V100 GPU. The initial learning rate was 0.0001 which was reduced with a factor of 0.7 if no improvement shown in 200 iterations. The training was stopped when the learning rate was below \(1\times 10^{-6}\)  or the max epoch number reached 100.
